rag-implementation

name rag-implementation description Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases. RAG Implementation Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources. When to Use This Skill Building Q&A systems over proprietary documents Creating chatbots with current, factual information Implementing semantic search with natural language queries Reducing hallucinations with grounded responses Enabling LLMs to access domain-specific knowledge Building documentation assistants Creating research tools with source citation Core Components 1. Vector Databases Purpose : Store and retrieve document embeddings efficiently Options: Pinecone : Managed, scalable, serverless Weaviate : Open-source, hybrid search, GraphQL Milvus : High performance, on-premise Chroma : Lightweight, easy to use, local development Qdrant : Fast, filtered search, Rust-based pgvector : PostgreSQL extension, SQL integration 2. Embeddings Purpose : Convert text to numerical vectors for similarity search Models (2026): Model Dimensions Best For voyage-3-large 1024 Claude apps (Anthropic recommended) voyage-code-3 1024 Code search text-embedding-3-large 3072 OpenAI apps, high accuracy text-embedding-3-small 1536 OpenAI apps, cost-effective bge-large-en-v1.5 1024 Open source, local deployment multilingual-e5-large 1024 Multi-language support 3. Retrieval Strategies Approaches: Dense Retrieval : Semantic similarity via embeddings Sparse Retrieval : Keyword matching (BM25, TF-IDF) Hybrid Search : Combine dense + sparse with weighted fusion Multi-Query : Generate multiple query variations HyDE : Generate hypothetical documents for better retrieval 4. Reranking Purpose : Improve retrieval quality by reordering results Methods: Cross-Encoders : BERT-based reranking (ms-marco-MiniLM) Cohere Rerank : API-based reranking Maximal Marginal Relevance (MMR) : Diversity + relevance LLM-based : Use LLM to score relevance Quick Start with LangGraph from langgraph.graph import StateGraph, START, END from langchain_anthropic import ChatAnthropic from langchain_voyageai import VoyageAIEmbeddings from langchain_pinecone import PineconeVectorStore from langchain_core.documents import Document from langchain_core.prompts import ChatPromptTemplate from langchain_text_splitters import RecursiveCharacterTextSplitter from typing import TypedDict, Annotated class RAGState ( TypedDict ): question: str context: list [Document] answer: str # Initialize components llm = ChatAnthropic(model= "claude-sonnet-5" ) embeddings = VoyageAIEmbeddings(model= "voyage-3-large" ) vectorstore = PineconeVectorStore(index_name= "docs" , embedding=embeddings) retriever = vectorstore.as_retriever(search_kwargs={ "k" : 4 }) # RAG prompt rag_prompt = ChatPromptTemplate.from_template( """Answer based on the context below. If you cannot answer, say so. Context: {context} Question: {question} Answer:""" ) async def retrieve ( state: RAGState ) -> RAGState: """Retrieve relevant documents.""" docs = await retriever.ainvoke(state[ "question" ]) return { "context" : docs} async def generate ( state: RAGState ) -> RAGState: """Generate answer from context.""" context_text = "\n\n" .join(doc.page_content for doc in state[ "context" ]) messages = rag_prompt.format_messages( context=context_text, question=state[ "question" ] ) response = await llm.ainvoke(messages) return { "answer" : response.content} # Build RAG graph builder = StateGraph(RAGState) builder.add_node( "retrieve" , retrieve) builder.add_node( "generate" , generate) builder.add_edge(START, "retrieve" ) builder.add_edge( "retrieve" , "generate" ) builder.add_edge( "generate" , END) rag_chain = builder. compile () # Use result = await rag_chain.ainvoke({ "question" : "What are the main features?" }) print (result[ "answer" ]) Detailed patterns and worked examples Detailed pattern documentation lives in references/details.md . Read that file when the navigation tier above is insufficient.